Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection

• URL: http://arxiv.org/abs/2012.13892v1
• Date: Sun, 27 Dec 2020 09:07:26 GMT
• Title: Adaptive Graph-based Generalized Regression Model for Unsupervised Feature Selection
• Authors: Yanyong Huang, Zongxin Shen, Fuxu Cai, Tianrui Li, Fengmao Lv
• Abstract summary: How to select the uncorrelated and discriminative features is the key problem of unsupervised feature selection. We present a novel generalized regression model imposed by an uncorrelated constraint and the $ell_2,1$-norm regularization. It can simultaneously select the uncorrelated and discriminative features as well as reduce the variance of these data points belonging to the same neighborhood.
• Score: 11.214334712819396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised feature selection is an important method to reduce dimensions of high dimensional data without labels, which is benefit to avoid ``curse of dimensionality'' and improve the performance of subsequent machine learning tasks, like clustering and retrieval. How to select the uncorrelated and discriminative features is the key problem of unsupervised feature selection. Many proposed methods select features with strong discriminant and high redundancy, or vice versa. However, they only satisfy one of these two criteria. Other existing methods choose the discriminative features with low redundancy by constructing the graph matrix on the original feature space. Since the original feature space usually contains redundancy and noise, it will degrade the performance of feature selection. In order to address these issues, we first present a novel generalized regression model imposed by an uncorrelated constraint and the $\ell_{2,1}$-norm regularization. It can simultaneously select the uncorrelated and discriminative features as well as reduce the variance of these data points belonging to the same neighborhood, which is help for the clustering task. Furthermore, the local intrinsic structure of data is constructed on the reduced dimensional space by learning the similarity-induced graph adaptively. Then the learnings of the graph structure and the indicator matrix based on the spectral analysis are integrated into the generalized regression model. Finally, we develop an alternative iterative optimization algorithm to solve the objective function. A series of experiments are carried out on nine real-world data sets to demonstrate the effectiveness of the proposed method in comparison with other competing approaches.

Related papers

• Feature Selection as Deep Sequential Generative Learning [50.00973409680637]
  We develop a deep variational transformer model over a joint of sequential reconstruction, variational, and performance evaluator losses. Our model can distill feature selection knowledge and learn a continuous embedding space to map feature selection decision sequences into embedding vectors associated with utility scores.
  arXiv  Detail & Related papers  (2024-03-06T16:31:56Z)
• Feature Selection Based on Orthogonal Constraints and Polygon Area [10.587608254638667]
  The goal of feature selection is to choose the optimal subset of features for a recognition task by evaluating the importance of each feature. This paper introduces a non-monotone linear search between dependencies enhancing features labels. Experimental results demonstrate that our approach not only effectively captures discriminative dependency but also surpasses traditional methods in reducing dimensions classification performance.
  arXiv  Detail & Related papers  (2024-02-25T08:20:05Z)
• Causal Feature Selection via Transfer Entropy [59.999594949050596]
  Causal discovery aims to identify causal relationships between features with observational data. We introduce a new causal feature selection approach that relies on the forward and backward feature selection procedures. We provide theoretical guarantees on the regression and classification errors for both the exact and the finite-sample cases.
  arXiv  Detail & Related papers  (2023-10-17T08:04:45Z)
• Nonlinear Feature Aggregation: Two Algorithms driven by Theory [45.3190496371625]
  Real-world machine learning applications are characterized by a huge number of features, leading to computational and memory issues. We propose a dimensionality reduction algorithm (NonLinCFA) which aggregates non-linear transformations of features with a generic aggregation function. We also test the algorithms on synthetic and real-world datasets, performing regression and classification tasks, showing competitive performances.
  arXiv  Detail & Related papers  (2023-06-19T19:57:33Z)
• Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation [71.27989298860481]
  We address the non- optimisation problem of finding a matrix on the Stiefel manifold that maximises a quadratic objective function. We propose a simple yet effective sparsity-promoting algorithm for finding the dominant eigenspace matrix.
  arXiv  Detail & Related papers  (2021-09-30T19:17:35Z)
• Low-rank Dictionary Learning for Unsupervised Feature Selection [11.634317251468968]
  We introduce a novel unsupervised feature selection approach by applying dictionary learning ideas in a low-rank representation. A unified objective function for unsupervised feature selection is proposed in a sparse way by an $ell_2,1$-norm regularization. Our experimental findings reveal that the proposed method outperforms the state-of-the-art algorithm.
  arXiv  Detail & Related papers  (2021-06-21T13:39:10Z)
• Top-$k$ Regularization for Supervised Feature Selection [11.927046591097623]
  We introduce a novel, simple yet effective regularization approach, named top-$k$ regularization, to supervised feature selection. We show that the top-$k$ regularization is effective and stable for supervised feature selection.
  arXiv  Detail & Related papers  (2021-06-04T01:12:47Z)
• Auto-weighted Multi-view Feature Selection with Graph Optimization [90.26124046530319]
  We propose a novel unsupervised multi-view feature selection model based on graph learning. The contributions are threefold: (1) during the feature selection procedure, the consensus similarity graph shared by different views is learned. Experiments on various datasets demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T03:25:25Z)
• Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature Selection [138.97647716793333]
  We propose a simple and efficient unsupervised feature selection method, by combining reconstruction error with $l_2,p$-norm regularization. We present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically.
  arXiv  Detail & Related papers  (2020-12-29T04:08:38Z)
• Joint Adaptive Graph and Structured Sparsity Regularization for Unsupervised Feature Selection [6.41804410246642]
  We propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method. A subset of optimal features will be selected in group, and the number of selected features will be determined automatically. Experimental results on eight benchmarks demonstrate the effectiveness and efficiency of the proposed method.
  arXiv  Detail & Related papers  (2020-10-09T08:17:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.